What the Data for This Category Looks Like
Data sources for paper industry investment research include public industry research reports, periodic financial reports of listed paper enterprises, daily price reports of raw materials (wood pulp, waste paper), monthly data on production capacity and operating rate, environmental policy documents, and demand research documents from the downstream packaging and printing industry. Update frequencies fall into three categories: research reports are updated quarterly or monthly, raw material prices are updated daily, and policy documents are released irregularly. Document structures include structured numerical fields and unstructured text fields. Structured fields mostly have clear units, such as yuan/ton, kg/ton pulp. Unstructured text mostly contains industry-specific professional terms and long sentence descriptions.
Constraints on Vector Models and Indexing From These Data Characteristics
Multi-source heterogeneous data formats require vector models to support mixed input, or format alignment during preprocessing to avoid semantic encoding bias across different data types. Frequently updated raw material price data requires indexes to support incremental writes, reducing computing resource consumption. Structured fields with clear units must retain unit information before vectorization, to prevent recall deviation caused by semantic confusion. Long-text research reports need segmentation adapted to the model's maximum context window, to avoid truncating key professional terms and data. Structured data associated across multiple fields requires configuring field association rules for the index, to ensure complete business logic is matched during recall.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Locally deployed Qwen3-Embedding-8B (or open-source embedding models of similar scale) | Supports a context window of 1024 or larger, and adapts to semantic encoding of paper industry professional terms |
chunk_size | 800–1200 characters | Adapts to the long-sentence structure of paper industry research reports, avoiding overly short segments that split professional terms, or overly long segments that exceed the model's context limit |
chunk_overlap | 100–150 characters | Retains contextual association between segments, preventing professional terms across segments from being split and losing semantic meaning |
index_type | IVFFlat index type from the FAISS library | Supports fast recall for millions of documents, and adapts to the incremental update needs of multi-source data |
top_k | Top 10–15 results | Covers multi-dimensional investment research data including raw materials, production capacity, policies, etc., avoiding too many irrelevant results or missing critical information |
similarity_threshold | 0.72–0.80 | Filters low-relevance non-professional documents, retaining research reports and data strongly related to the paper industry |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing the settings.
Three Common Mistakes
- Symptom: A 500 error is returned when calling the locally deployed Qwen3-Embedding-8B, with a prompt indicating model connection timeout. Cause: The correct VLLM service port was not filled in the FastGPT embedding model configuration, or internal service communication is restricted by a firewall.
- Symptom: The knowledge base disk usage statistics only show the size of original files and split chunks, and do not include embedding vector usage data. Cause: Disk monitoring statistics for vector storage were not enabled, or vector data size statistics were not enabled in the database configuration.
- Symptom:
top_kis set to 5, but only 2 results are returned during recall. Cause: Thesimilarity_thresholdis set too high, filtering most matching results, or business fields of multi-source data were not correctly associated during index construction.
How to Confirm Configuration Is Complete
- Execute the FastGPT built-in embedding model test interface, input a paper industry professional term such as "softwood pulp consumption rate", and confirm the returned vector data is not empty and matches the dimension declared by the model.
- Check the knowledge base disk usage statistics panel, and confirm the statistics include usage for original files, split chunks, and embedding vectors.
- Submit a query related to paper production capacity, verify the number of recall results matches the
top_kparameter setting, and that similarity scores fall within the presetsimilarity_thresholdrange. - Manually modify the content of one split document chunk, trigger an incremental index update, and confirm updated query results match the modified content.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.